In this work, we present a method to synthesize stabilizing RNN controllers, which ensures the stability of the feedback systems both during learning and control process.
We develop a convex set of stabilizing RNN parameters for nonlinear and partially observed systems. A novel projected policy gradient method is developed to synthesize a controller while enforcing stability by recursively projecting the parameters of the RNN controller to the convex set. By evaluating on a variety of control tasks, we demonstrate that our method learns stabilizing controllers with fewer samples, faster converging trajectories, and higher final performance than policy gradient.
Future directions include extensions to implicit models [1]}, [2]} or other memory units.
